CUTTING STATE MONITORING IN MILLING BY A NEURAL-NETWORK

Abstract

The application of a neural network to cutting state monitoring in face milling was introduced and evaluated on multiple sensor data such as cutting forces and vibrations. This monitoring system consists of a statistically based adaptive preprocessor (autoregressive (AR) time series modeling) for generating features from each sensor, followed by a highly parallel neural network for associating the preprocessor outputs (sensor fusion) with the appropriate decisions. AR model parameters were used as features, and the cutting states (normal, unstable and tool life end) were successfully detected by monitoring the evolution of model parameters during face milling. The proposed system offers fast operation through recursive preprocessing and highly parallel association, and a data-driven training scheme without explicit rules or a priori statistics. It appears proven on limited experimental data.